Visible to the public Biblio

Found 7036 results

2019-07-01
Savola, Reijo M., Savolainen, Pekka.  2018.  Risk-driven Security Metrics Development for Software-defined Networking. Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings. :56:1–56:5.
Introduction of SDN (Software-Defined Networking) into the network management turns the formerly quite rigid networks to programmatically reconfigurable, dynamic and high-performing entities, which are managed remotely. At the same time, introduction of the new interfaces evidently widens the attack surface, and new kind of attack vectors are introduced threatening the QoS even critically. Thus, there is need for a security architecture, drawing from the SDN management and monitoring capabilities, and eventually covering the threats posed by the SDN evolution. For efficient security-architecture implementation, we analyze the security risks of SDN and based on that propose heuristic security objectives. Further, we decompose the objectives for effective security control implementation and security metrics definition to support informed security decision-making and continuous security improvement.
Šišejković, Dominik, Leupers, Rainer, Ascheid, Gerd, Metzner, Simon.  2018.  A Unifying Logic Encryption Security Metric. Proceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation. :179–186.
The globalization of the IC supply chain has brought forth the era of fabless companies. Due to security issues during design and fabrication processes, various security concerns have risen, ranging from IP piracy and reverse engineering to hardware Trojans. Logic encryption has emerged as a mitigation against these threats. However, no generic metrics for quantifying the security of logic encryption algorithms has been reported so far, making it impossible to formally compare different approaches. In this paper, we propose a unifying metric, capturing the key security aspects of logic encryption algorithms. The metric is evaluated on state-of-the-art algorithms and benchmarks.
Clemente, C. J., Jaafar, F., Malik, Y..  2018.  Is Predicting Software Security Bugs Using Deep Learning Better Than the Traditional Machine Learning Algorithms? 2018 IEEE International Conference on Software Quality, Reliability and Security (QRS). :95–102.
Software insecurity is being identified as one of the leading causes of security breaches. In this paper, we revisited one of the strategies in solving software insecurity, which is the use of software quality metrics. We utilized a multilayer deep feedforward network in examining whether there is a combination of metrics that can predict the appearance of security-related bugs. We also applied the traditional machine learning algorithms such as decision tree, random forest, naïve bayes, and support vector machines and compared the results with that of the Deep Learning technique. The results have successfully demonstrated that it was possible to develop an effective predictive model to forecast software insecurity based on the software metrics and using Deep Learning. All the models generated have shown an accuracy of more than sixty percent with Deep Learning leading the list. This finding proved that utilizing Deep Learning methods and a combination of software metrics can be tapped to create a better forecasting model thereby aiding software developers in predicting security bugs.
Kebande, V. R., Kigwana, I., Venter, H. S., Karie, N. M., Wario, R. D..  2018.  CVSS Metric-Based Analysis, Classification and Assessment of Computer Network Threats and Vulnerabilities. 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). :1–10.
This paper provides a Common Vulnerability Scoring System (CVSS) metric-based technique for classifying and analysing the prevailing Computer Network Security Vulnerabilities and Threats (CNSVT). The problem that is addressed in this paper, is that, at the time of writing this paper, there existed no effective approaches for analysing and classifying CNSVT for purposes of assessments based on CVSS metrics. The authors of this paper have achieved this by generating a CVSS metric-based dynamic Vulnerability Analysis Classification Countermeasure (VACC) criterion that is able to rank vulnerabilities. The CVSS metric-based VACC has allowed the computation of vulnerability Similarity Measure (VSM) using the Hamming and Euclidean distance metric functions. Nevertheless, the CVSS-metric based on VACC also enabled the random measuring of the VSM for a selected number of vulnerabilities based on the [Ma-Ma], [Ma-Mi], [Mi-Ci], [Ma-Ci] ranking score. This is a technique that is aimed at allowing security experts to be able to conduct proper vulnerability detection and assessments across computer-based networks based on the perceived occurrence by checking the probability that given threats will occur or not. The authors have also proposed high-level countermeasures of the vulnerabilities that have been listed. The authors have evaluated the CVSS-metric based VACC and the results are promising. Based on this technique, it is worth noting that these propositions can help in the development of stronger computer and network security tools.
Medeiros, N., Ivaki, N., Costa, P., Vieira, M..  2018.  An Approach for Trustworthiness Benchmarking Using Software Metrics. 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). :84–93.
Trustworthiness is a paramount concern for users and customers in the selection of a software solution, specially in the context of complex and dynamic environments, such as Cloud and IoT. However, assessing and benchmarking trustworthiness (worthiness of software for being trusted) is a challenging task, mainly due to the variety of application scenarios (e.g., businesscritical, safety-critical), the large number of determinative quality attributes (e.g., security, performance), and last, but foremost, due to the subjective notion of trust and trustworthiness. In this paper, we present trustworthiness as a measurable notion in relative terms based on security attributes and propose an approach for the assessment and benchmarking of software. The main goal is to build a trustworthiness assessment model based on software metrics (e.g., Cyclomatic Complexity, CountLine, CBO) that can be used as indicators of software security. To demonstrate the proposed approach, we assessed and ranked several files and functions of the Mozilla Firefox project based on their trustworthiness score and conducted a survey among several software security experts in order to validate the obtained rank. Results show that our approach is able to provide a sound ranking of the benchmarked software.
Meryem, Amar, Samira, Douzi, Bouabid, El Ouahidi.  2018.  Enhancing Cloud Security Using Advanced MapReduce K-means on Log Files. Proceedings of the 2018 International Conference on Software Engineering and Information Management. :63–67.
Many customers ranked cloud security as a major challenge that threaten their work and reduces their trust on cloud service's provider. Hence, a significant improvement is required to establish better adaptations of security measures that suit recent technologies and especially distributed architectures. Considering the meaningful recorded data in cloud generated log files, making analysis on them, mines insightful value about hacker's activities. It identifies malicious user behaviors and predicts new suspected events. Not only that, but centralizing log files, prevents insiders from causing damage to system. In this paper, we proposed to take away sensitive log files into a single server provider and combining both MapReduce programming and k-means on the same algorithm to cluster observed events into classes having similar features. To label unknown user behaviors and predict new suspected activities this approach considers cosine distances and deviation metrics.
Pope, Aaron Scott, Morning, Robert, Tauritz, Daniel R., Kent, Alexander D..  2018.  Automated Design of Network Security Metrics. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :1680–1687.
Many abstract security measurements are based on characteristics of a graph that represents the network. These are typically simple and quick to compute but are often of little practical use in making real-world predictions. Practical network security is often measured using simulation or real-world exercises. These approaches better represent realistic outcomes but can be costly and time-consuming. This work aims to combine the strengths of these two approaches, developing efficient heuristics that accurately predict attack success. Hyper-heuristic machine learning techniques, trained on network attack simulation training data, are used to produce novel graph-based security metrics. These low-cost metrics serve as an approximation for simulation when measuring network security in real time. The approach is tested and verified using a simulation based on activity from an actual large enterprise network. The results demonstrate the potential of using hyper-heuristic techniques to rapidly evolve and react to emerging cybersecurity threats.
Ahmed, Yussuf, Naqvi, Syed, Josephs, Mark.  2018.  Aggregation of Security Metrics for Decision Making: A Reference Architecture. Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings. :53:1–53:7.
Existing security technologies play a significant role in protecting enterprise systems but they are no longer enough on their own given the number of successful cyberattacks against businesses and the sophistication of the tactics used by attackers to bypass the security defences. Security measurement is different to security monitoring in the sense that it provides a means to quantify the security of the systems while security monitoring helps in identifying abnormal events and does not measure the actual state of an infrastructure's security. The goal of enterprise security metrics is to enable understanding of the overall security using measurements to guide decision making. In this paper we present a reference architecture for aggregating the measurement values from the different components of the system in order to enable stakeholders to see the overall security state of their enterprise systems and to assist with decision making. This will provide a newer dimension to security management by shifting from security monitoring to security measurement.
Zieger, A., Freiling, F., Kossakowski, K..  2018.  The β-Time-to-Compromise Metric for Practical Cyber Security Risk Estimation. 2018 11th International Conference on IT Security Incident Management IT Forensics (IMF). :115–133.
To manage cybersecurity risks in practice, a simple yet effective method to assess suchs risks for individual systems is needed. With time-to-compromise (TTC), McQueen et al. (2005) introduced such a metric that measures the expected time that a system remains uncompromised given a specific threat landscape. Unlike other approaches that require complex system modeling to proceed, TTC combines simplicity with expressiveness and therefore has evolved into one of the most successful cybersecurity metrics in practice. We revisit TTC and identify several mathematical and methodological shortcomings which we address by embedding all aspects of the metric into the continuous domain and the possibility to incorporate information about vulnerability characteristics and other cyber threat intelligence into the model. We propose β-TTC, a formal extension of TTC which includes information from CVSS vectors as well as a continuous attacker skill based on a β-distribution. We show that our new metric (1) remains simple enough for practical use and (2) gives more realistic predictions than the original TTC by using data from a modern and productively used vulnerability database of a national CERT.
Carrasco, A., Ropero, J., Clavijo, P. Ruiz de, Benjumea, J., Luque, A..  2018.  A Proposal for a New Way of Classifying Network Security Metrics: Study of the Information Collected through a Honeypot. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :633–634.
Nowadays, honeypots are a key tool to attract attackers and study their activity. They help us in the tasks of evaluating attacker's behaviour, discovering new types of attacks, and collecting information and statistics associated with them. However, the gathered data cannot be directly interpreted, but must be analyzed to obtain useful information. In this paper, we present a SSH honeypot-based system designed to simulate a vulnerable server. Thus, we propose an approach for the classification of metrics from the data collected by the honeypot along 19 months.
Arabsorkhi, A., Ghaffari, F..  2018.  Security Metrics: Principles and Security Assessment Methods. 2018 9th International Symposium on Telecommunications (IST). :305–310.
Nowadays, Information Technology is one of the important parts of human life and also of organizations. Organizations face problems such as IT problems. To solve these problems, they have to improve their security sections. Thus there is a need for security assessments within organizations to ensure security conditions. The use of security standards and general metric can be useful for measuring the safety of an organization; however, it should be noted that the general metric which are applied to businesses in general cannot be effective in this particular situation. Thus it's important to select metric standards for different businesses to improve both cost and organizational security. The selection of suitable security measures lies in the use of an efficient way to identify them. Due to the numerous complexities of these metric and the extent to which they are defined, in this paper that is based on comparative study and the benchmarking method, taxonomy for security measures is considered to be helpful for a business to choose metric tailored to their needs and conditions.
Akhtar, T., Gupta, B. B., Yamaguchi, S..  2018.  Malware propagation effects on SCADA system and smart power grid. 2018 IEEE International Conference on Consumer Electronics (ICCE). :1–6.

Critical infrastructures have suffered from different kind of cyber attacks over the years. Many of these attacks are performed using malwares by exploiting the vulnerabilities of these resources. Smart power grid is one of the major victim which suffered from these attacks and its SCADA system are frequently targeted. In this paper we describe our proposed framework to analyze smart power grid, while its SCADA system is under attack by malware. Malware propagation and its effects on SCADA system is the focal point of our analysis. OMNeT++ simulator and openDSS is used for developing and analyzing the simulated smart power grid environment.

Urias, V. E., Stout, M. S. William, Leeuwen, B. V..  2018.  On the Feasibility of Generating Deception Environments for Industrial Control Systems. 2018 IEEE International Symposium on Technologies for Homeland Security (HST). :1–6.

The cyber threat landscape is a constantly morphing surface; the need for cyber defenders to develop and create proactive threat intelligence is on the rise, especially on critical infrastructure environments. It is commonly voiced that Supervisory Control and Data Acquisition (SCADA) systems and Industrial Control Systems (ICS) are vulnerable to the same classes of threats as other networked computer systems. However, cyber defense in operational ICS is difficult, often introducing unacceptable risks of disruption to critical physical processes. This is exacerbated by the notion that hardware used in ICS is often expensive, making full-scale mock-up systems for testing and/or cyber defense impractical. New paradigms in cyber security have focused heavily on using deception to not only protect assets, but also gather insight into adversary motives and tools. Much of the work that we see in today's literature is focused on creating deception environments for traditional IT enterprise networks; however, leveraging our prior work in the domain, we explore the opportunities, challenges and feasibility of doing deception in ICS networks.

Perez, R. Lopez, Adamsky, F., Soua, R., Engel, T..  2018.  Machine Learning for Reliable Network Attack Detection in SCADA Systems. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :633–638.

Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F1score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F1score of respectively \textbackslashtextgreater 99%.

Zabetian-Hosseini, A., Mehrizi-Sani, A., Liu, C..  2018.  Cyberattack to Cyber-Physical Model of Wind Farm SCADA. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. :4929–4934.

In recent years, there has been a significant increase in wind power penetration into the power system. As a result, the behavior of the power system has become more dependent on wind power behavior. Supervisory control and data acquisition (SCADA) systems responsible for monitoring and controlling wind farms often have vulnerabilities that make them susceptible to cyberattacks. These vulnerabilities allow attackers to exploit and intrude in the wind farm SCADA system. In this paper, a cyber-physical system (CPS) model for the information and communication technology (ICT) model of the wind farm SCADA system integrated with SCADA of the power system is proposed. Cybersecurity of this wind farm SCADA system is discussed. Proposed cyberattack scenarios on the system are modeled and the impact of these cyberattacks on the behavior of the power systems on the IEEE 9-bus modified system is investigated. Finally, an anomaly attack detection algorithm is proposed to stop the attack of tripping of all wind farms. Case studies validate the performance of the proposed CPS model of the test system and the attack detection algorithm.

Kolosok, I., Korkina, E., Mahnitko, A., Gavrilovs, A..  2018.  Supporting Cyber-Physical Security of Electric Power System by the State Estimation Technique. 2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). :1–6.

Security is one of the most important properties of electric power system (EPS). We consider the state estimation (SE) tool as a barrier to the corruption of data on current operating conditions of the EPS. An algorithm for a two-level SE on the basis of SCADA and WAMS measurements is effective in terms of detection of malicious attacks on energy system. The article suggests a methodology to identify cyberattacks on SCADA and WAMS.

Kumar, S., Gaur, N., Kumar, A..  2018.  Developing a Secure Cyber Ecosystem for SCADA Architecture. 2018 Second International Conference on Computing Methodologies and Communication (ICCMC). :559–562.

Advent of Cyber has converted the entire World into a Global village. But, due to vurneabilites in SCADA architecture [1] national assests are more prone to cyber attacks.. Cyber invasions have a catastrophic effect in the minds of the civilian population, in terms of states security system. A robust cyber security is need of the hour to protect the critical information infastructrue & critical infrastructure of a country. Here, in this paper we scrutinize cyber terrorism, vurneabilites in SCADA network systems [1], [2] and concept of cyber resilience to combat cyber attacks.

Senthivel, Saranyan, Dhungana, Shrey, Yoo, Hyunguk, Ahmed, Irfan, Roussev, Vassil.  2018.  Denial of Engineering Operations Attacks in Industrial Control Systems. Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy. :319–329.
We present a new type of attack termed denial of engineering operations in which an attacker can interfere with the normal cycle of an engineering operation leading to a loss of situational awareness. Specifically, the attacker can deceive the engineering software during attempts to retrieve the ladder logic program from a programmable logic controller (PLC) by manipulating the ladder logic on the PLC, such that the software is unable to process it while the PLC continues to execute it successfully. This attack vector can provide sufficient cover for the attacker»s actual scenario to play out while the owner tries to understand the problem and reestablish positive operational control. To enable the forensic analysis and, eventually, eliminate the threat, we have developed the first decompiler for ladder logic programs. Ladder logic is a graphical programming language for PLCs that control physical processes such as power grid, pipelines, and chemical plants; PLCs are a common target of malicious modifications leading to the compromise of the control behavior (and potentially serious consequences). Our decompiler, Laddis, transforms a low-level representation to its corresponding high-level original representation comprising of graphical symbols and connections. The evaluation of the accuracy of the decompiler on the program of varying complexity demonstrates perfect reconstruction of the original program. We present three new attack scenarios on PLC-deployed ladder logic and demonstrate the effectiveness of the decompiler on these scenarios.
de Lima, Davi Ferreira, Bezerra, José Roberto, de Macedo Costa da Silva, Jorge Fredericson.  2018.  Privacy and Integrity Protection of Data in SCADA Systems. Proceedings of the 10th Latin America Networking Conference. :110–114.
The critical infrastructure of a country, due to its relevance and complexity, demands systems to facilitate the user interaction to correctly deal and share the information related to the monitored processes. The systems currently applied to monitor such infrastructures are based on SCADA architecture. The advent of the Internet jointly to the needs of fast information exchange at any place in order to support accurate decision-making processes, demands increasing interconnection among SCADA and management systems. However, such interconnection may expose sensitive data manipulated by such systems to hackers which may cause massive damages, financial loses, threats to human lives among others. Therefore, unprotected data may expose the systems to cyber-criminals by affecting any of the cyber-security principles, Confidentiality, Integrity, and Authenticity. This article presents four study cases using Wireshark to analyze a popular SCADA software to check user authentication process. The result of this research was the reading in plain text of the user authentication data used to access the control and monitoring system. As an immediate and minimal solution, this work presents low cost, easy setup and open source solutions are proposed using VPN and protocol obfuscator to improve security applying cryptography and hide the data passing through Virtual Private Network.
Almehmadi, Abdulaziz.  2018.  SCADA Networks Anomaly-based Intrusion Detection System. Proceedings of the 11th International Conference on Security of Information and Networks. :18:1–18:4.
Intentional attacks1 that cause country wide blackouts, gas and water systems malfunction are actions that can be carried out by a nation to impact on another nation in a mean of war. Supervisory control and data acquisition (SCADA) networks that allow for communication for the utilities companies were designed with no security in mind causing the systems that a nation relies on to fall vulnerable to exploitation. Since SCADA networks are static in nature with pre-defined signatures of network traffic, we propose to design an anomaly-based intrusion detection system to detect abnormality in SCADA network traffic and protocols. We gather normal SCADA network traffic via tapping on the network for 30 days and then attack the network using Denial of Service (DoS) attack, message spoofing attack and man-in-the middle attack. We then train a classifier with two classes, normal and abnormal and report the classifier accuracy in detecting abnormal SCADA network traffic.
Ammar, Zakariya, AlSharif, Ahmad.  2018.  Deployment of IoT-based Honeynet Model. Proceedings of the 6th International Conference on Information Technology: IoT and Smart City. :134–139.
This paper deals with the developing model of a honeynet that depends on the Internet of things (IoT). Due to significant of industrial services, such model helps enhancement of information security detection in industrial domain, the model is designed to detect adversaries whom attempt to attack industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems. The model consists of hardware and software aspects, designed to focus on ICS services that managed remotely via SCADA systems. In order to prove the work of the model, a few of security tools are used such as Shodan, Nmap and others. These tools have been applied locally inside LAN and globally via internet to get proving results. Ultimately, results contain a list of protocols and ports that represent industry control services. To clarify outputs, it contains tcp/udp ports 623, 102, 1025 and 161 which represent respectively IPMI, S7comm, KAMSTRAP and SNMP services.
Nwebonyi, Francis N., Martins, Rolando, Correia, Manuel E..  2018.  Reputation-Based Security System For Edge Computing. Proceedings of the 13th International Conference on Availability, Reliability and Security. :39:1-39:8.

Given the centralized architecture of cloud computing, there is a genuine concern about its ability to adequately cope with the demands of connecting devices which are sharply increasing in number and capacity. This has led to the emergence of edge computing technologies, including but not limited to mobile edge-clouds. As a branch of Peer-to-Peer (P2P) networks, mobile edge-clouds inherits disturbing security concerns which have not been adequately addressed in previous methods. P2P security systems have featured many trust-based methods owing to their suitability and cost advantage, but these approaches still lack in a number of ways. They mostly focus on protecting client nodes from malicious service providers, but downplay the security of service provider nodes, thereby creating potential loopholes for bandwidth attack. Similarly, trust bootstrapping is often via default scores, or based on heuristics that does not reflect the identity of a newcomer. This work has patched these inherent loopholes and improved fairness among participating peers. The use cases of mobile edge-clouds have been particularly considered and a scalable reputation based security mechanism was derived to suit them. BitTorrent protocol was modified to form a suitable test bed, using Peersim simulator. The proposed method was compared to some related methods in the literature through detailed simulations. Results show that the new method can foster trust and significantly improve network security, in comparison to previous similar systems.

Liu, Changming, Zou, Deqing, Luo, Peng, Zhu, Bin B., Jin, Hai.  2018.  A Heuristic Framework to Detect Concurrency Vulnerabilities. Proceedings of the 34th Annual Computer Security Applications Conference. :529-541.

With a growing demand of concurrent software to exploit multi-core hardware capability, concurrency vulnerabilities have become an inevitable threat to the security of today's IT industry. Existing concurrent program detection schemes focus mainly on detecting concurrency errors such as data races, atomicity violation, etc., with little attention paid to detect concurrency vulnerabilities that may be exploited to infringe security. In this paper, we propose a heuristic framework that combines both static analysis and fuzz testing to detect targeted concurrency vulnerabilities such as concurrency buffer overflow, double free, and use-after-free. The static analysis locates sensitive concurrent operations in a concurrent program, categorizes each finding into a potential type of concurrency vulnerability, and determines the execution order of the sensitive operations in each finding that would trigger the suspected concurrency vulnerability. The results are then plugged into the fuzzer with the execution order fixed by the static analysis in order to trigger the suspected concurrency vulnerabilities. In order to introduce more variance which increases possibility that the concurrency errors can be triggered, we also propose manipulation of thread scheduling priority to enable a fuzzer such as AFL to effectively explore thread interleavings in testing a concurrent program. To the best of our knowledge, this is the first fuzzer that is capable of effectively exploring concurrency errors. In evaluating the proposed heuristic framework with a benchmark suit of six real-world concurrent C programs, the framework detected two concurrency vulnerabilities for the proposed concurrency vulnerability detection, both being confirmed to be true positives, and produced three new crashes for the proposed interleaving exploring fuzzer that existing fuzzers could not produce. These results demonstrate the power and effectiveness of the proposed heuristic framework in detecting concurrency errors and vulnerabilities.

Napoli, Daniela.  2018.  Developing Accessible and Usable Security (ACCUS) Heuristics. Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. :SRC16:1-SRC16:6.

Currently, usable security and web accessibility design principles exist separately. Although literature at the intersect of accessibility and security is developing, it is limited in its understanding of how users with vision loss operate the web securely. In this paper, we propose heuristics that fuse the nuances of both fields. With these heuristics, we evaluate 10 websites and uncover several issues that can impede users' ability to abide by common security advice.

Amjad, N., Afzal, H., Amjad, M. F., Khan, F. A..  2018.  A Multi-Classifier Framework for Open Source Malware Forensics. 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :106-111.

Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.